from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 1s

KMeans_short: 0h 0m 2s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 10s

LogisticRegression: 0h 0m 19s

KMeans_tall: 0h 0m 22s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 27s

daal4py_KNeighborsClassifier: 0h 2m 24s

KNeighborsClassifier_kd_tree: 0h 2m 34s

lightgbm: 0h 5m 4s

HistGradientBoostingClassifier: 0h 5m 6s

catboost_lossguide: 0h 5m 9s

xgboost: 0h 5m 19s

catboost_symmetric: 0h 5m 19s

KNeighborsClassifier: 0h 32m 21s

total: 1h 4m 58s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.744	0.000	-1	100	NaN	NaN	0.501	0.000	0.278	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	30.387	0.000	0.000	0.030	-1	100	0.941	0.935	1.755	0.011	17.313	0.108	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.179	0.014	0.000	0.179	-1	100	1.000	1.000	0.090	0.000	1.997	0.161	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.149	0.000	-1	1	NaN	NaN	0.485	0.000	0.268	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.893	0.244	0.000	0.025	-1	1	0.692	0.713	1.689	0.007	14.738	0.156	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.175	0.012	0.000	0.175	-1	1	0.000	0.000	0.095	0.011	1.846	0.242	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.771	0.000	1	100	NaN	NaN	0.486	0.000	0.285	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	19.666	0.186	0.000	0.020	1	100	0.941	0.935	1.759	0.012	11.181	0.129	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.202	0.001	0.000	0.202	1	100	1.000	1.000	0.090	0.000	2.247	0.013	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.000	6.094	0.000	-1	5	NaN	NaN	0.485	0.000	0.271	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	30.062	0.167	0.000	0.030	-1	5	0.822	0.806	1.693	0.008	17.760	0.132	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.018	0.000	0.183	-1	5	1.000	1.000	0.091	0.000	2.021	0.201	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.136	0.000	5.862	0.000	1	5	NaN	NaN	0.485	0.000	0.281	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	19.749	0.352	0.000	0.020	1	5	0.822	0.713	1.692	0.010	11.670	0.220	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.208	0.002	0.000	0.208	1	5	1.000	0.000	0.091	0.001	2.300	0.024	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.000	5.917	0.000	1	1	NaN	NaN	0.485	0.000	0.278	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.464	0.195	0.000	0.013	1	1	0.692	0.806	1.690	0.007	7.965	0.120	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.203	0.001	0.000	0.203	1	1	0.000	1.000	0.095	0.010	2.146	0.233	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.286	0.000	-1	100	NaN	NaN	0.098	0.000	0.568	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.772	0.000	0.000	0.031	-1	100	0.986	0.985	0.302	0.002	101.743	0.759	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.002	0.000	0.029	-1	100	1.000	1.000	0.005	0.001	5.483	0.616	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.286	0.000	-1	1	NaN	NaN	0.099	0.000	0.562	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	22.260	0.117	0.000	0.022	-1	1	0.975	0.980	0.254	0.001	87.491	0.555	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.003	0.000	0.019	-1	1	1.000	1.000	0.005	0.000	3.769	0.636	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.294	0.000	1	100	NaN	NaN	0.098	0.000	0.553	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.201	0.528	0.000	0.018	1	100	0.986	0.985	0.303	0.002	60.081	1.804	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	1	100	1.000	1.000	0.005	0.000	4.000	0.393	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.051	0.000	0.314	0.000	-1	5	NaN	NaN	0.099	0.000	0.515	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.500	0.000	0.000	0.030	-1	5	0.981	0.984	0.258	0.002	118.374	0.715	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.002	0.000	0.029	-1	5	1.000	1.000	0.005	0.001	5.876	0.773	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.296	0.000	1	5	NaN	NaN	0.099	0.000	0.545	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.224	0.044	0.000	0.019	1	5	0.981	0.980	0.255	0.001	75.514	0.319	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.001	0.000	0.022	1	5	1.000	1.000	0.005	0.000	4.477	0.465	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.293	0.000	1	1	NaN	NaN	0.099	0.000	0.553	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.780	0.005	0.000	0.011	1	1	0.975	0.984	0.256	0.001	42.110	0.112	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.001	2.853	0.315	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.799	0.000	0.029	0.000	1	100	NaN	NaN	0.709	0.000	3.949	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.667	0.008	0.000	0.005	1	100	0.978	0.974	0.184	0.003	25.354	0.395	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.000	0.000	9.943	5.496	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.831	0.000	0.028	0.000	-1	5	NaN	NaN	0.671	0.000	4.218	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.808	0.006	0.000	0.001	-1	5	0.976	0.963	0.100	0.001	8.054	0.096	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	10.026	5.172	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.804	0.000	0.029	0.000	1	1	NaN	NaN	0.672	0.000	4.172	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.738	0.005	0.000	0.001	1	1	0.954	0.974	0.186	0.002	3.964	0.054	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.828	1.485	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.848	0.000	0.028	0.000	1	5	NaN	NaN	0.682	0.000	4.176	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.409	0.009	0.000	0.001	1	5	0.976	0.969	0.539	0.012	2.616	0.060	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	5	1.000	1.000	0.001	0.000	1.494	0.723	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.876	0.000	0.028	0.000	-1	1	NaN	NaN	0.693	0.000	4.150	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.432	0.005	0.000	0.000	-1	1	0.954	0.969	0.533	0.004	0.811	0.011	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	2.272	0.951	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.957	0.000	0.027	0.000	-1	100	NaN	NaN	0.671	0.000	4.407	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.722	0.032	0.000	0.003	-1	100	0.978	0.963	0.100	0.001	27.158	0.385	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	-1	100	1.000	1.000	0.000	0.000	18.861	9.501	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.715	0.000	0.022	0.000	1	100	NaN	NaN	0.427	0.000	1.675	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.003	0.000	0.000	1	100	0.984	0.983	0.001	0.000	42.852	12.674	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.642	4.458	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.714	0.000	0.022	0.000	-1	5	NaN	NaN	0.427	0.000	1.674	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	5	0.982	0.976	0.001	0.000	35.545	10.221	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	19.443	14.759	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.000	0.022	0.000	1	1	NaN	NaN	0.464	0.000	1.553	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	1	0.977	0.983	0.001	0.000	21.664	6.308	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.629	4.534	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.000	0.022	0.000	1	5	NaN	NaN	0.436	0.000	1.653	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	1	5	0.982	0.985	0.006	0.001	3.925	0.439	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.136	4.126	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.730	0.000	0.022	0.000	-1	1	NaN	NaN	0.428	0.000	1.705	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	-1	1	0.977	0.985	0.006	0.001	3.920	0.524	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	18.744	14.277	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.734	0.000	0.022	0.000	-1	100	NaN	NaN	0.433	0.000	1.694	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.042	0.003	0.000	0.000	-1	100	0.984	0.976	0.001	0.000	56.556	19.792	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.708	17.103	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.623	0.0	0.771	0.000	k-means++	NaN	30	NaN	0.409	0.0	1.523	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.377	0.000	k-means++	0.000	30	0.001	0.000	0.0	8.521	5.436	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.311	8.181	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.496	0.0	0.967	0.000	random	NaN	30	NaN	0.353	0.0	1.405	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.381	0.000	random	0.001	30	0.001	0.000	0.0	9.235	6.658	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.825	8.362	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.550	0.0	3.664	0.000	k-means++	NaN	30	NaN	3.096	0.0	2.116	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.617	0.000	k-means++	0.002	30	0.003	0.000	0.0	5.880	2.944	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.353	7.734	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.869	0.0	4.089	0.000	random	NaN	30	NaN	2.940	0.0	1.996	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.800	0.000	random	0.003	30	0.002	0.000	0.0	5.719	3.094	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	10.287	7.725	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.073	0.0	0.044	0.000	random	NaN	20	NaN	0.031	0.0	2.355	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.190	0.000	random	0.002	20	0.004	0.001	0.0	2.689	0.484	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	10.180	7.546	See	See
3	KMeans_short	fit	10000	10000	2	20	0.219	0.0	0.015	0.000	k-means++	NaN	20	NaN	0.083	0.0	2.624	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.192	0.000	k-means++	-0.000	20	0.002	0.001	0.0	2.613	0.610	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.159	7.500	See	See
6	KMeans_short	fit	10000	10000	100	20	0.190	0.0	0.844	0.000	random	NaN	20	NaN	0.127	0.0	1.498	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.663	0.000	random	0.344	20	0.324	0.001	0.0	2.182	0.329	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.989	4.859	See	See
9	KMeans_short	fit	10000	10000	100	20	0.562	0.0	0.285	0.000	k-means++	NaN	20	NaN	0.314	0.0	1.788	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.631	0.000	k-means++	0.338	20	0.316	0.001	0.0	2.204	0.357	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.218	5.050	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.018	0.0	[-0.10708741]	0.000	NaN	NaN	NaN	NaN	NaN	2.023	0.0	5.446	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.5941921]	0.000	NaN	NaN	NaN	NaN	0.546	0.000	0.0	0.888	0.510	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.21355686]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.422	0.377	See	See
3	LogisticRegression	fit	1000	1000	10000	[34]	0.989	0.0	[-1.59278656]	0.001	NaN	NaN	NaN	NaN	NaN	0.727	0.0	1.360	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[34]	0.002	0.0	[172.06900068]	0.000	NaN	NaN	NaN	NaN	0.240	0.003	0.0	0.556	0.137	See	See
5	LogisticRegression	predict	1000	1	10000	[34]	0.000	0.0	[29.61295219]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.127	0.101	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.178	0.000	0.449	0.0	NaN	NaN	NaN	0.178	0.000	1.002	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.001	6.961	0.0	NaN	NaN	0.095	0.019	0.001	0.616	0.037	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.206	0.0	NaN	NaN	NaN	0.000	0.000	0.715	0.749	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.444	0.000	0.554	0.0	NaN	NaN	NaN	0.238	0.000	6.061	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	5.818	0.0	NaN	NaN	1.000	0.000	0.000	0.633	0.485	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.015	0.0	NaN	NaN	NaN	0.000	0.000	0.665	0.743	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves